Pandas Profiling Report

Dataset statistics

Number of variables	24
Number of observations	30828
Missing cells	64412
Missing cells (%)	8.7%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	6.5 MiB
Average record size in memory	220.3 B

Variable types

Text	6
Numeric	9
Categorical	6
Boolean	1
DateTime	2

Alerts

`width` has constant value ""	Constant
`height` has constant value ""	Constant
`id` is highly overall correlated with `pub_year` and 1 other fields	High correlation
`price` is highly overall correlated with `updated_price` and 2 other fields	High correlation
`pub_year` is highly overall correlated with `id` and 1 other fields	High correlation
`year_of_sale` is highly overall correlated with `id` and 1 other fields	High correlation
`updated_price` is highly overall correlated with `price` and 2 other fields	High correlation
`edition_num` is highly overall correlated with `edition`	High correlation
`price_range` is highly overall correlated with `price` and 1 other fields	High correlation
`edition` is highly overall correlated with `edition_num`	High correlation
`affordability` is highly overall correlated with `price` and 1 other fields	High correlation
`edition_num` is highly imbalanced (54.7%)	Imbalance
`availabe` is highly imbalanced (93.3%)	Imbalance
`price_range` is highly imbalanced (69.0%)	Imbalance
`edition` is highly imbalanced (54.7%)	Imbalance
`edition_num` has 27974 (90.7%) missing values	Missing
`description` has 4232 (13.7%) missing values	Missing
`edition` has 27974 (90.7%) missing values	Missing
`Length_of_description` has 4232 (13.7%) missing values	Missing

Reproduction

Analysis started	2024-03-17 14:16:19.057578
Analysis finished	2024-03-17 14:16:36.637334
Duration	17.58 seconds
Software version	ydata-profiling v0.0.dev0
Download configuration	config.json

Title
Text

Distinct	26227
Distinct (%)	85.1%
Missing	0
Missing (%)	0.0%
Memory size	1.5 MiB

Length

Max length	248
Median length	142
Mean length	38.113533
Min length	1

Characters and Unicode

Total characters	1174964
Distinct characters	129
Distinct categories	16 ?
Distinct scripts	3 ?
Distinct blocks	8 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	24249 ?
Unique (%)	78.7%

Sample

1st row	Learning Go
2nd row	Tidy First?
3rd row	Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
4th row	Designing Data-Intensive Applications
5th row	Exam Ref MS-102 Microsoft 365 Administrator

Value	Count	Frequency (%)
and	8757	5.6%
in	3946	2.5%
for	3677	2.3%
the	2844	1.8%
of	2653	1.7%
with	2286	1.5%
systems	2012	1.3%
data	1758	1.1%
learning	1421	0.9%
applications	1386	0.9%
Other values (10962)	125972	80.4%

Most occurring characters

Value	Count	Frequency (%)
	125945	10.7%
e	98215	8.4%
n	88067	7.5%
i	86892	7.4%
t	71112	6.1%
o	70641	6.0%
a	69477	5.9%
r	58227	5.0%
s	48064	4.1%
l	36083	3.1%
Other values (119)	422241	35.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	871187	74.1%
Uppercase Letter	147997	12.6%
Space Separator	125956	10.7%
Decimal Number	15154	1.3%
Other Punctuation	7955	0.7%
Dash Punctuation	4620	0.4%
Open Punctuation	637	0.1%
Close Punctuation	634	0.1%
Math Symbol	577	< 0.1%
Other Symbol	126	< 0.1%
Other values (6)	121	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	98215	11.3%
n	88067	10.1%
i	86892	10.0%
t	71112	8.2%
o	70641	8.1%
a	69477	8.0%
r	58227	6.7%
s	48064	5.5%
l	36083	4.1%
c	35308	4.1%
Other values (33)	209101	24.0%

Uppercase Letter

Value	Count	Frequency (%)
S	16559	11.2%
A	14458	9.8%
C	14126	9.5%
P	11457	7.7%
I	10507	7.1%
D	10049	6.8%
M	10040	6.8%
T	8676	5.9%
E	7246	4.9%
B	4963	3.4%
Other values (22)	39916	27.0%

Other Punctuation

Value	Count	Frequency (%)
,	3392	42.6%
:	1672	21.0%
.	1515	19.0%
'	546	6.9%
#	254	3.2%
&	222	2.8%
/	200	2.5%
!	82	1.0%
?	39	0.5%
*	16	0.2%
Other values (5)	17	0.2%

Decimal Number

Value	Count	Frequency (%)
0	4079	26.9%
2	3881	25.6%
1	2596	17.1%
3	1020	6.7%
5	738	4.9%
4	690	4.6%
6	617	4.1%
7	550	3.6%
8	532	3.5%
9	451	3.0%

Math Symbol

Value	Count	Frequency (%)
+	569	98.6%
¬	3	0.5%
−	3	0.5%
∞	1	0.2%
≥	1	0.2%

Open Punctuation

Value	Count	Frequency (%)
(	630	98.9%
[	4	0.6%
‚	2	0.3%
„	1	0.2%

Dash Punctuation

Value	Count	Frequency (%)
-	4138	89.6%
–	468	10.1%
—	14	0.3%

Other Symbol

Value	Count	Frequency (%)
®	109	86.5%
™	15	11.9%
°	2	1.6%

Other Number

Value	Count	Frequency (%)
½	2	50.0%
⁵	1	25.0%
²	1	25.0%

Space Separator

Value	Count	Frequency (%)
	125945	> 99.9%
	11	< 0.1%

Close Punctuation

Value	Count	Frequency (%)
)	630	99.4%
]	4	0.6%

Final Punctuation

Value	Count	Frequency (%)
’	92	92.9%
”	7	7.1%

Initial Punctuation

Value	Count	Frequency (%)
“	10	71.4%
‘	4	28.6%

Modifier Symbol

Value	Count	Frequency (%)
´	2	100.0%

Format

Value	Count	Frequency (%)
	1	100.0%

Currency Symbol

Value	Count	Frequency (%)
€	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	1019183	86.7%
Common	155780	13.3%
Cyrillic	1	< 0.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	98215	9.6%
n	88067	8.6%
i	86892	8.5%
t	71112	7.0%
o	70641	6.9%
a	69477	6.8%
r	58227	5.7%
s	48064	4.7%
l	36083	3.5%
c	35308	3.5%
Other values (64)	357097	35.0%

Common

Value	Count	Frequency (%)
	125945	80.8%
-	4138	2.7%
0	4079	2.6%
2	3881	2.5%
,	3392	2.2%
1	2596	1.7%
:	1672	1.1%
.	1515	1.0%
3	1020	0.7%
5	738	0.5%
Other values (44)	6804	4.4%

Cyrillic

Value	Count	Frequency (%)
С	1	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1173935	99.9%
Punctuation	601	0.1%
None	405	< 0.1%
Letterlike Symbols	15	< 0.1%
Math Operators	5	< 0.1%
Currency Symbols	1	< 0.1%
Cyrillic	1	< 0.1%
Alphabetic PF	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	125945	10.7%
e	98215	8.4%
n	88067	7.5%
i	86892	7.4%
t	71112	6.1%
o	70641	6.0%
a	69477	5.9%
r	58227	5.0%
s	48064	4.1%
l	36083	3.1%
Other values (72)	421212	35.9%

Punctuation

Value	Count	Frequency (%)
–	468	77.9%
’	92	15.3%
—	14	2.3%
“	10	1.7%
”	7	1.2%
‘	4	0.7%
…	3	0.5%
‚	2	0.3%
„	1	0.2%

None

Value	Count	Frequency (%)
ü	201	49.6%
®	109	26.9%
ä	22	5.4%
	11	2.7%
ß	6	1.5%
Ü	5	1.2%
ö	5	1.2%
é	5	1.2%
ó	4	1.0%
Æ	3	0.7%
Other values (21)	34	8.4%

Letterlike Symbols

Value	Count	Frequency (%)
™	15	100.0%

Math Operators

Value	Count	Frequency (%)
−	3	60.0%
∞	1	20.0%
≥	1	20.0%

Currency Symbols

Value	Count	Frequency (%)
€	1	100.0%

Cyrillic

Value	Count	Frequency (%)
С	1	100.0%

Alphabetic PF

Value	Count	Frequency (%)
ﬁ	1	100.0%

id
Real number (ℝ)

HIGH CORRELATION

Distinct	30819
Distinct (%)	> 99.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	90451061

Minimum	106017
Maximum	2.112314 × 10⁸
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.5 MiB

Quantile statistics

Minimum	106017
5-th percentile	333777.3
Q1	1316881.8
median	95611996
Q3	2.1003473 × 10⁸
95-th percentile	2.107434 × 10⁸
Maximum	2.112314 × 10⁸
Range	2.1112538 × 10⁸
Interquartile range (IQR)	2.0871785 × 10⁸

Descriptive statistics

Standard deviation	95145835
Coefficient of variation (CV)	1.051904
Kurtosis	-1.7238003
Mean	90451061
Median Absolute Deviation (MAD)	94880937
Skewness	0.30935755
Sum	2.7884253 × 10¹²
Variance	9.05273 × 10¹⁵
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
969139	2	< 0.1%
2487727	2	< 0.1%
2487867	2	< 0.1%
1632606	2	< 0.1%
211138211	2	< 0.1%
2502407	2	< 0.1%
2510123	2	< 0.1%
209634307	2	< 0.1%
210450539	2	< 0.1%
211190367	1	< 0.1%
Other values (30809)	30809	99.9%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
106017	1	< 0.1%
116047	1	< 0.1%
116386	1	< 0.1%
116387	1	< 0.1%
116991	1	< 0.1%
117479	1	< 0.1%
117483	1	< 0.1%
117501	1	< 0.1%
117536	1	< 0.1%
117736	1	< 0.1%

Value	Count	Frequency (%)
211231399	1	< 0.1%
211231398	1	< 0.1%
211231397	1	< 0.1%
211231034	1	< 0.1%
211231021	1	< 0.1%
211231012	1	< 0.1%
211230710	1	< 0.1%
211230697	1	< 0.1%
211230632	1	< 0.1%
211230596	1	< 0.1%

price
Real number (ℝ)

HIGH CORRELATION

Distinct	1307
Distinct (%)	4.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	87.497864

Minimum	0
Maximum	2353.3694
Zeros	108
Zeros (%)	0.4%
Negative	0
Negative (%)	0.0%
Memory size	1.5 MiB

Quantile statistics

Minimum	0
5-th percentile	26.9381
Q1	49.99
median	72.641169
Q3	94.437879
95-th percentile	217.95257
Maximum	2353.3694
Range	2353.3694
Interquartile range (IQR)	44.447879

Descriptive statistics

Standard deviation	69.133156
Coefficient of variation (CV)	0.79011249
Kurtosis	75.309387
Mean	87.497864
Median Absolute Deviation (MAD)	22.651169
Skewness	4.7905371
Sum	2697384.2
Variance	4779.3932
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
72.64116886	4441	14.4%
130.7657289	1796	5.8%
49.99	893	2.9%
203.4214289	716	2.3%
55.99	671	2.2%
44.99	669	2.2%
83.3021061	502	1.6%
63.82076688	467	1.5%
77.39285164	440	1.4%
76.5005461	419	1.4%
Other values (1297)	19814	64.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	108	0.4%
0.99	1	< 0.1%
1.33343595	6	< 0.1%
1.99	3	< 0.1%
2.68034095	8	< 0.1%
2.99	5	< 0.1%
3.99	9	< 0.1%
4.02724595	7	< 0.1%
4.2339711	3	< 0.1%
4.74377196	1	< 0.1%

Value	Count	Frequency (%)
2353.369398	1	< 0.1%
1878.93095	1	< 0.1%
1615.3705	1	< 0.1%
1360.312	1	< 0.1%
1055.97352	1	< 0.1%
935.2145	1	< 0.1%
892.70475	1	< 0.1%
850.195	1	< 0.1%
783.228446	1	< 0.1%
781.2049	1	< 0.1%

author
Text

Distinct	20384
Distinct (%)	66.1%
Missing	0
Missing (%)	0.0%
Memory size	1.5 MiB

Length

Max length	106
Median length	42
Mean length	14.213248
Min length	2

Characters and Unicode

Total characters	438166
Distinct characters	162
Distinct categories	17 ?
Distinct scripts	3 ?
Distinct blocks	5 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	15649 ?
Unique (%)	50.8%

Sample

1st row	Jon Bodner
2nd row	Kent Beck
3rd row	Aurélien Géron
4th row	Martin Kleppmann
5th row	Orin Thomas

Value	Count	Frequency (%)
david	524	0.8%
michael	495	0.7%
a	426	0.6%
j	386	0.6%
john	383	0.6%
m	380	0.6%
r	305	0.4%
paul	293	0.4%
s	290	0.4%
peter	286	0.4%
Other values (20622)	64807	94.5%

Most occurring characters

Value	Count	Frequency (%)
a	43807	10.0%
	37765	8.6%
e	34379	7.8%
i	28866	6.6%
n	28771	6.6%
r	27110	6.2%
o	22219	5.1%
l	17661	4.0%
s	15353	3.5%
h	14214	3.2%
Other values (152)	168021	38.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	323792	73.9%
Uppercase Letter	70732	16.1%
Space Separator	37765	8.6%
Other Punctuation	4510	1.0%
Dash Punctuation	1244	0.3%
Close Punctuation	37	< 0.1%
Open Punctuation	37	< 0.1%
Math Symbol	16	< 0.1%
Decimal Number	12	< 0.1%
Final Punctuation	11	< 0.1%
Other values (7)	10	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	43807	13.5%
e	34379	10.6%
i	28866	8.9%
n	28771	8.9%
r	27110	8.4%
o	22219	6.9%
l	17661	5.5%
s	15353	4.7%
h	14214	4.4%
t	14029	4.3%
Other values (74)	77383	23.9%

Uppercase Letter

Value	Count	Frequency (%)
M	6589	9.3%
S	6287	8.9%
A	5195	7.3%
J	4510	6.4%
B	4318	6.1%
C	4288	6.1%
D	3924	5.5%
R	3895	5.5%
P	3456	4.9%
K	3267	4.6%
Other values (38)	25003	35.3%

Decimal Number

Value	Count	Frequency (%)
3	4	33.3%
2	3	25.0%
1	2	16.7%
4	1	8.3%
9	1	8.3%
5	1	8.3%

Other Punctuation

Value	Count	Frequency (%)
.	4304	95.4%
'	103	2.3%
,	89	2.0%
"	12	0.3%
&	2	< 0.1%

Math Symbol

Value	Count	Frequency (%)
√	4	25.0%
∆	4	25.0%
¬	4	25.0%
≈	3	18.8%
≠	1	6.2%

Final Punctuation

Value	Count	Frequency (%)
’	10	90.9%
”	1	9.1%

Format

Value	Count	Frequency (%)
	1	50.0%
‌	1	50.0%

Space Separator

Value	Count	Frequency (%)
	37765	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	1244	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	37	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	37	100.0%

Other Symbol

Value	Count	Frequency (%)
°	2	100.0%

Nonspacing Mark

Value	Count	Frequency (%)
̌	2	100.0%

Currency Symbol

Value	Count	Frequency (%)
£	1	100.0%

Initial Punctuation

Value	Count	Frequency (%)
“	1	100.0%

Other Number

Value	Count	Frequency (%)
²	1	100.0%

Modifier Symbol

Value	Count	Frequency (%)
¨	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	394524	90.0%
Common	43639	10.0%
Inherited	3	< 0.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	43807	11.1%
e	34379	8.7%
i	28866	7.3%
n	28771	7.3%
r	27110	6.9%
o	22219	5.6%
l	17661	4.5%
s	15353	3.9%
h	14214	3.6%
t	14029	3.6%
Other values (122)	148115	37.5%

Common

Value	Count	Frequency (%)
	37765	86.5%
.	4304	9.9%
-	1244	2.9%
'	103	0.2%
,	89	0.2%
)	37	0.1%
(	37	0.1%
"	12	< 0.1%
’	10	< 0.1%
√	4	< 0.1%
Other values (18)	34	0.1%

Inherited

Value	Count	Frequency (%)
̌	2	66.7%
‌	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	436301	99.6%
None	1837	0.4%
Punctuation	14	< 0.1%
Math Operators	12	< 0.1%
Diacriticals	2	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	43807	10.0%
	37765	8.7%
e	34379	7.9%
i	28866	6.6%
n	28771	6.6%
r	27110	6.2%
o	22219	5.1%
l	17661	4.0%
s	15353	3.5%
h	14214	3.3%
Other values (57)	166156	38.1%

None

Value	Count	Frequency (%)
é	282	15.4%
ü	194	10.6%
á	185	10.1%
ö	143	7.8%
í	143	7.8%
ó	104	5.7%
ä	81	4.4%
ł	60	3.3%
Á	43	2.3%
ç	42	2.3%
Other values (75)	560	30.5%

Punctuation

Value	Count	Frequency (%)
’	10	71.4%
“	1	7.1%
	1	7.1%
‌	1	7.1%
”	1	7.1%

Math Operators

Value	Count	Frequency (%)
√	4	33.3%
∆	4	33.3%
≈	3	25.0%
≠	1	8.3%

Diacriticals

Value	Count	Frequency (%)
̌	2	100.0%

publisher
Text

Distinct	194
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Memory size	1.5 MiB

Length

Max length	68
Median length	50
Mean length	19.600266
Min length	3

Characters and Unicode

Total characters	604237
Distinct characters	60
Distinct categories	8 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	93 ?
Unique (%)	0.3%

Sample

1st row	O'Reilly Media
2nd row	O'Reilly Media
3rd row	O'Reilly Media
4th row	O'Reilly Media
5th row	Pearson Education

Value	Count	Frequency (%)
publishing	13082	18.5%
springer	10968	15.5%
international	8444	11.9%
packt	3923	5.5%
press	3411	4.8%
apress	3061	4.3%
crc	2719	3.8%
berlin	2350	3.3%
heidelberg	2350	3.3%
education	2145	3.0%
Other values (304)	18284	25.8%

Most occurring characters

Value	Count	Frequency (%)
i	65112	10.8%
n	60394	10.0%
e	50243	8.3%
r	48796	8.1%
	39913	6.6%
l	35711	5.9%
s	30877	5.1%
a	30452	5.0%
g	27076	4.5%
t	24668	4.1%
Other values (50)	190995	31.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	482167	79.8%
Uppercase Letter	80026	13.2%
Space Separator	39913	6.6%
Other Punctuation	2098	0.3%
Dash Punctuation	19	< 0.1%
Math Symbol	6	< 0.1%
Open Punctuation	4	< 0.1%
Close Punctuation	4	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
i	65112	13.5%
n	60394	12.5%
e	50243	10.4%
r	48796	10.1%
l	35711	7.4%
s	30877	6.4%
a	30452	6.3%
g	27076	5.6%
t	24668	5.1%
b	16001	3.3%
Other values (15)	92837	19.3%

Uppercase Letter

Value	Count	Frequency (%)
P	22844	28.5%
S	13039	16.3%
I	8629	10.8%
C	7236	9.0%
R	4481	5.6%
E	3463	4.3%
A	3196	4.0%
H	3156	3.9%
B	2615	3.3%
W	2606	3.3%
Other values (15)	8761	10.9%

Other Punctuation

Value	Count	Frequency (%)
'	1702	81.1%
&	240	11.4%
,	59	2.8%
.	53	2.5%
!	44	2.1%

Space Separator

Value	Count	Frequency (%)
	39913	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	19	100.0%

Math Symbol

Value	Count	Frequency (%)
+	6	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	4	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	4	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	562193	93.0%
Common	42044	7.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
i	65112	11.6%
n	60394	10.7%
e	50243	8.9%
r	48796	8.7%
l	35711	6.4%
s	30877	5.5%
a	30452	5.4%
g	27076	4.8%
t	24668	4.4%
P	22844	4.1%
Other values (40)	166020	29.5%

Common

Value	Count	Frequency (%)
	39913	94.9%
'	1702	4.0%
&	240	0.6%
,	59	0.1%
.	53	0.1%
!	44	0.1%
-	19	< 0.1%
+	6	< 0.1%
(	4	< 0.1%
)	4	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	604235	> 99.9%
None	2	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
i	65112	10.8%
n	60394	10.0%
e	50243	8.3%
r	48796	8.1%
	39913	6.6%
l	35711	5.9%
s	30877	5.1%
a	30452	5.0%
g	27076	4.5%
t	24668	4.1%
Other values (49)	190993	31.6%

None

Value	Count	Frequency (%)
ä	2	100.0%

pub_year
Real number (ℝ)

HIGH CORRELATION

Distinct	42
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2011.6573

Minimum	1753
Maximum	2025
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.5 MiB

Quantile statistics

Minimum	1753
5-th percentile	2003
Q1	2012
median	2016
Q3	2020
95-th percentile	2023
Maximum	2025
Range	272
Interquartile range (IQR)	8

Descriptive statistics

Standard deviation	31.568664
Coefficient of variation (CV)	0.015692863
Kurtosis	61.051632
Mean	2011.6573
Median Absolute Deviation (MAD)	4
Skewness	-7.8082054
Sum	62015372
Variance	996.58053
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=42)

Value	Count	Frequency (%)
2022	2350	7.6%
2018	2284	7.4%
2017	2232	7.2%
2016	2080	6.7%
2020	2065	6.7%
2021	2058	6.7%
2019	2047	6.6%
2015	1902	6.2%
2013	1889	6.1%
2014	1760	5.7%
Other values (32)	10161	33.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1753	438	1.4%
1980	1	< 0.1%
1986	4	< 0.1%
1987	1	< 0.1%
1988	6	< 0.1%
1989	9	< 0.1%
1990	12	< 0.1%
1991	15	< 0.1%
1992	12	< 0.1%
1993	22	0.1%

Value	Count	Frequency (%)
2025	21	0.1%
2024	316	1.0%
2023	1485	4.8%
2022	2350	7.6%
2021	2058	6.7%
2020	2065	6.7%
2019	2047	6.6%
2018	2284	7.4%
2017	2232	7.2%
2016	2080	6.7%

edition_num
Categorical

HIGH CORRELATION IMBALANCE MISSING

Distinct	21
Distinct (%)	0.7%
Missing	27974
Missing (%)	90.7%
Memory size	1.5 MiB

(2nd ed.)	1653
(3rd ed.)	540
(4th ed.)	260
(5th ed.)	160
(6th ed.)	90
Other values (16)	151

Length

Max length	11
Median length	10
Mean length	10.015767
Min length	10

Characters and Unicode

Total characters	28585
Distinct characters	21
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	6 ?
Unique (%)	0.2%

Sample

1st row	(2nd ed.)
2nd row	(3rd ed.)
3rd row	(8th ed.)
4th row	(5th ed.)
5th row	(2nd ed.)

Common Values

Value	Count	Frequency (%)
(2nd ed.)	1653	5.4%
(3rd ed.)	540	1.8%
(4th ed.)	260	0.8%
(5th ed.)	160	0.5%
(6th ed.)	90	0.3%
(7th ed.)	54	0.2%
(8th ed.)	32	0.1%
(9th ed.)	20	0.1%
(10th ed.)	14	< 0.1%
(11th ed.)	7	< 0.1%
Other values (11)	24	0.1%
(Missing)	27974	90.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
ed	2854	50.0%
2nd	1653	29.0%
3rd	540	9.5%
4th	260	4.6%
5th	160	2.8%
6th	90	1.6%
7th	54	0.9%
8th	32	0.6%
9th	20	0.4%
10th	14	0.2%
Other values (12)	31	0.5%

Most occurring characters

Value	Count	Frequency (%)
	5708	20.0%
d	5048	17.7%
e	2854	10.0%
.	2854	10.0%
)	2854	10.0%
(	2854	10.0%
2	1663	5.8%
n	1654	5.8%
t	660	2.3%
h	658	2.3%
Other values (11)	1778	6.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	11416	39.9%
Space Separator	5708	20.0%
Decimal Number	2899	10.1%
Other Punctuation	2854	10.0%
Close Punctuation	2854	10.0%
Open Punctuation	2854	10.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
2	1663	57.4%
3	543	18.7%
4	265	9.1%
5	163	5.6%
6	91	3.1%
7	55	1.9%
1	50	1.7%
8	33	1.1%
9	22	0.8%
0	14	0.5%

Lowercase Letter

Value	Count	Frequency (%)
d	5048	44.2%
e	2854	25.0%
n	1654	14.5%
t	660	5.8%
h	658	5.8%
r	540	4.7%
s	2	< 0.1%

Space Separator

Value	Count	Frequency (%)
	5708	100.0%

Other Punctuation

Value	Count	Frequency (%)
.	2854	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	2854	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	2854	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	17169	60.1%
Latin	11416	39.9%

Most frequent character per script

Common

Value	Count	Frequency (%)
	5708	33.2%
.	2854	16.6%
)	2854	16.6%
(	2854	16.6%
2	1663	9.7%
3	543	3.2%
4	265	1.5%
5	163	0.9%
6	91	0.5%
7	55	0.3%
Other values (4)	119	0.7%

Latin

Value	Count	Frequency (%)
d	5048	44.2%
e	2854	25.0%
n	1654	14.5%
t	660	5.8%
h	658	5.8%
r	540	4.7%
s	2	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	28585	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	5708	20.0%
d	5048	17.7%
e	2854	10.0%
.	2854	10.0%
)	2854	10.0%
(	2854	10.0%
2	1663	5.8%
n	1654	5.8%
t	660	2.3%
h	658	2.3%
Other values (11)	1778	6.2%

description
Text

MISSING

Distinct	25729
Distinct (%)	96.7%
Missing	4232
Missing (%)	13.7%
Memory size	1.5 MiB

Length

Max length	350
Median length	344
Mean length	266.82223
Min length	6

Characters and Unicode

Total characters	7096404
Distinct characters	187
Distinct categories	18 ?
Distinct scripts	3 ?
Distinct blocks	9 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	25139 ?
Unique (%)	94.5%

Sample

1st row	Go has rapidly become the preferred language for building web services. Plenty of tutorials are available to teach Go's syntax to developers with experience in other programming languages, but tutorials aren't enough. They don't teach Go's idioms, so developers end up recreating patterns that don't make sense in a Go context. This practical...
2nd row	Tidying up messy software is a must. And that means breaking up the code to make it more readable, and using guard clauses and helping functions to make it understandable. In this practical guide, author Kent Beck, creator of Extreme Programming and pioneer of software patterns, suggests when and where you might apply tidyings in your code. ...
3rd row	Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready...
4th row	Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What...
5th row	Prepare for Microsoft Exam MS-102 and help demonstrate your real-world mastery of skills and knowledge required to deploy and manage Microsoft 365 and perform Microsoft 365 tenant-level implementation and administration of cloud and hybrid...

Value	Count	Frequency (%)
the	57597	5.5%
and	43430	4.2%
of	34273	3.3%
in	26279	2.5%
to	23825	2.3%
this	17961	1.7%
a	16551	1.6%
book	13242	1.3%
on	12187	1.2%
for	11113	1.1%
Other values (42743)	781576	75.3%

Most occurring characters

Value	Count	Frequency (%)
	1026204	14.5%
e	683046	9.6%
t	462942	6.5%
n	442988	6.2%
o	439771	6.2%
i	427951	6.0%
a	401346	5.7%
s	369472	5.2%
r	346435	4.9%
l	220716	3.1%
Other values (177)	2275533	32.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	5445507	76.7%
Space Separator	1027546	14.5%
Uppercase Letter	280785	4.0%
Other Punctuation	203869	2.9%
Decimal Number	99567	1.4%
Dash Punctuation	24978	0.4%
Open Punctuation	4398	0.1%
Close Punctuation	4257	0.1%
Final Punctuation	3285	< 0.1%
Math Symbol	1083	< 0.1%
Other values (8)	1129	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	683046	12.5%
t	462942	8.5%
n	442988	8.1%
o	439771	8.1%
i	427951	7.9%
a	401346	7.4%
s	369472	6.8%
r	346435	6.4%
l	220716	4.1%
c	208089	3.8%
Other values (58)	1442751	26.5%

Uppercase Letter

Value	Count	Frequency (%)
T	36569	13.0%
I	29263	10.4%
S	26992	9.6%
C	26524	9.4%
A	22778	8.1%
P	15160	5.4%
M	13590	4.8%
D	12418	4.4%
E	11596	4.1%
L	9243	3.3%
Other values (28)	76652	27.3%

Other Punctuation

Value	Count	Frequency (%)
.	107568	52.8%
,	71021	34.8%
;	6583	3.2%
&	6103	3.0%
'	3445	1.7%
:	2682	1.3%
"	1425	0.7%
?	1408	0.7%
/	1290	0.6%
!	873	0.4%
Other values (11)	1471	0.7%

Decimal Number

Value	Count	Frequency (%)
2	24155	24.3%
1	22112	22.2%
0	20480	20.6%
3	5436	5.5%
9	4990	5.0%
5	4799	4.8%
4	4661	4.7%
6	4482	4.5%
8	4291	4.3%
7	4161	4.2%

Math Symbol

Value	Count	Frequency (%)
+	1048	96.8%
>	17	1.6%
=	4	0.4%
∞	4	0.4%
<	4	0.4%
\|	2	0.2%
~	2	0.2%
−	1	0.1%
√	1	0.1%

Dash Punctuation

Value	Count	Frequency (%)
-	23021	92.2%
—	1092	4.4%
–	854	3.4%
―	5	< 0.1%
‐	5	< 0.1%
‑	1	< 0.1%

Other Symbol

Value	Count	Frequency (%)
®	276	72.8%
™	59	15.6%
©	17	4.5%
°	14	3.7%
�	12	3.2%
─	1	0.3%

Space Separator

Value	Count	Frequency (%)
	1026204	99.9%
	1309	0.1%
	31	< 0.1%
	2	< 0.1%

Open Punctuation

Value	Count	Frequency (%)
(	4337	98.6%
„	31	0.7%
[	26	0.6%
{	4	0.1%

Close Punctuation

Value	Count	Frequency (%)
)	4228	99.3%
]	26	0.6%
}	3	0.1%

Final Punctuation

Value	Count	Frequency (%)
’	2942	89.6%
”	339	10.3%
»	4	0.1%

Initial Punctuation

Value	Count	Frequency (%)
“	445	85.1%
‘	74	14.1%
«	4	0.8%

Modifier Symbol

Value	Count	Frequency (%)
`	12	70.6%
´	4	23.5%
˜	1	5.9%

Other Number

Value	Count	Frequency (%)
²	4	57.1%
½	2	28.6%
¾	1	14.3%

Format

Value	Count	Frequency (%)
	81	51.9%
	75	48.1%

Currency Symbol

Value	Count	Frequency (%)
$	30	78.9%
¢	8	21.1%

Connector Punctuation

Value	Count	Frequency (%)
_	7	100.0%

Control

Value	Count	Frequency (%)
	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	5726284	80.7%
Common	1370117	19.3%
Greek	3	< 0.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	683046	11.9%
t	462942	8.1%
n	442988	7.7%
o	439771	7.7%
i	427951	7.5%
a	401346	7.0%
s	369472	6.5%
r	346435	6.0%
l	220716	3.9%
c	208089	3.6%
Other values (91)	1723528	30.1%

Common

Value	Count	Frequency (%)
	1026204	74.9%
.	107568	7.9%
,	71021	5.2%
2	24155	1.8%
-	23021	1.7%
1	22112	1.6%
0	20480	1.5%
;	6583	0.5%
&	6103	0.4%
3	5436	0.4%
Other values (73)	57434	4.2%

Greek

Value	Count	Frequency (%)
Ω	1	33.3%
λ	1	33.3%
α	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	7086666	99.9%
Punctuation	5959	0.1%
None	3679	0.1%
Letterlike Symbols	63	< 0.1%
Alphabetic PF	17	< 0.1%
Specials	12	< 0.1%
Math Operators	6	< 0.1%
Box Drawing	1	< 0.1%
Modifier Letters	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1026204	14.5%
e	683046	9.6%
t	462942	6.5%
n	442988	6.3%
o	439771	6.2%
i	427951	6.0%
a	401346	5.7%
s	369472	5.2%
r	346435	4.9%
l	220716	3.1%
Other values (84)	2265795	32.0%

Punctuation

Value	Count	Frequency (%)
’	2942	49.4%
—	1092	18.3%
–	854	14.3%
“	445	7.5%
”	339	5.7%
	75	1.3%
‘	74	1.2%
…	50	0.8%
•	42	0.7%
„	31	0.5%
Other values (5)	15	0.3%

None

Value	Count	Frequency (%)
	1309	35.6%
ü	690	18.8%
ä	495	13.5%
®	276	7.5%
ö	228	6.2%
	81	2.2%
ß	81	2.2%
Ü	53	1.4%
é	51	1.4%
â	40	1.1%
Other values (57)	375	10.2%

Letterlike Symbols

Value	Count	Frequency (%)
™	59	93.7%
ℤ	4	6.3%

Specials

Value	Count	Frequency (%)
�	12	100.0%

Alphabetic PF

Value	Count	Frequency (%)
ﬁ	10	58.8%
ﬀ	6	35.3%
ﬂ	1	5.9%

Math Operators

Value	Count	Frequency (%)
∞	4	66.7%
−	1	16.7%
√	1	16.7%

Box Drawing

Value	Count	Frequency (%)
─	1	100.0%

Modifier Letters

Value	Count	Frequency (%)
˜	1	100.0%

availabe
Boolean

IMBALANCE

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	1.3 MiB

True	30583
False	245

Common Values (Table)
Common Values (Plot)

Value	Count	Frequency (%)
True	30583	99.2%
False	245	0.8%

sale_date
Date

Distinct	6476
Distinct (%)	21.0%
Missing	0
Missing (%)	0.0%
Memory size	1.5 MiB

Minimum	1753-01-01 00:00:00
Maximum	2025-02-01 00:00:00

Histogram

Histogram with fixed size bins (bins=50)

short_pub
Date

Distinct	425
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Memory size	1.5 MiB

Minimum	1753-01-01 00:00:00
Maximum	2025-02-01 00:00:00

Histogram

Histogram with fixed size bins (bins=50)

num_of_author
Real number (ℝ)

Distinct	24
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.2194758

Minimum	1
Maximum	59
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.5 MiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1
median	2
Q3	3
95-th percentile	5
Maximum	59
Range	58
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.7157742
Coefficient of variation (CV)	0.77305382
Kurtosis	53.04646
Mean	2.2194758
Median Absolute Deviation (MAD)	1
Skewness	3.8186391
Sum	68422
Variance	2.9438812
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=24)

Value	Count	Frequency (%)
1	13849	44.9%
2	7472	24.2%
3	4407	14.3%
4	2554	8.3%
5	1264	4.1%
6	576	1.9%
7	247	0.8%
8	170	0.6%
10	82	0.3%
9	81	0.3%
Other values (14)	126	0.4%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	13849	44.9%
2	7472	24.2%
3	4407	14.3%
4	2554	8.3%
5	1264	4.1%
6	576	1.9%
7	247	0.8%
8	170	0.6%
9	81	0.3%
10	82	0.3%

Value	Count	Frequency (%)
59	1	< 0.1%
30	1	< 0.1%
24	1	< 0.1%
22	1	< 0.1%
20	3	< 0.1%
19	2	< 0.1%
18	3	< 0.1%
17	9	< 0.1%
16	8	< 0.1%
15	10	< 0.1%

width
Categorical

CONSTANT

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	1.5 MiB

97	30828

Length

Max length	2
Median length	2
Mean length	2
Min length	2

Characters and Unicode

Total characters	61656
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	97
2nd row	97
3rd row	97
4th row	97
5th row	97

Common Values

Value	Count	Frequency (%)
97	30828	100.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
97	30828	100.0%

Most occurring characters

Value	Count	Frequency (%)
9	30828	50.0%
7	30828	50.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	61656	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
9	30828	50.0%
7	30828	50.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	61656	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
9	30828	50.0%
7	30828	50.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	61656	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
9	30828	50.0%
7	30828	50.0%

height
Categorical

CONSTANT

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	1.5 MiB

150	30828

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	92484
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	150
2nd row	150
3rd row	150
4th row	150
5th row	150

Common Values

Value	Count	Frequency (%)
150	30828	100.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
150	30828	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	30828	33.3%
5	30828	33.3%
0	30828	33.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	92484	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	30828	33.3%
5	30828	33.3%
0	30828	33.3%

Most occurring scripts

Value	Count	Frequency (%)
Common	92484	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	30828	33.3%
5	30828	33.3%
0	30828	33.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	92484	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	30828	33.3%
5	30828	33.3%
0	30828	33.3%

processed_title
Text

Distinct	24823
Distinct (%)	80.5%
Missing	0
Missing (%)	0.0%
Memory size	1.5 MiB

Length

Max length	228
Median length	137
Mean length	37.017354
Min length	0

Characters and Unicode

Total characters	1141171
Distinct characters	49
Distinct categories	3 ?
Distinct scripts	3 ?
Distinct blocks	4 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	22210 ?
Unique (%)	72.0%

Sample

1st row	learning go
2nd row	tidy first
3rd row	hands on machine learning with scikit learn keras and tensorflow
4th row	designing data intensive applications
5th row	exam ref ms microsoft administrator

Value	Count	Frequency (%)
and	8760	5.7%
in	4103	2.7%
for	3677	2.4%
the	2851	1.9%
of	2662	1.7%
with	2286	1.5%
systems	2027	1.3%
data	1816	1.2%
learning	1454	1.0%
applications	1387	0.9%
Other values (9281)	121385	79.6%

Most occurring characters

Value	Count	Frequency (%)
	125213	11.0%
e	105323	9.2%
i	97278	8.5%
n	91635	8.0%
a	83097	7.3%
t	79748	7.0%
o	74501	6.5%
s	64065	5.6%
r	62783	5.5%
c	48829	4.3%
Other values (39)	308699	27.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	1015954	89.0%
Space Separator	125213	11.0%
Other Number	4	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	105323	10.4%
i	97278	9.6%
n	91635	9.0%
a	83097	8.2%
t	79748	7.8%
o	74501	7.3%
s	64065	6.3%
r	62783	6.2%
c	48829	4.8%
l	41038	4.0%
Other values (35)	267657	26.3%

Other Number

Value	Count	Frequency (%)
½	2	50.0%
²	1	25.0%
⁵	1	25.0%

Space Separator

Value	Count	Frequency (%)
	125213	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	1015953	89.0%
Common	125217	11.0%
Cyrillic	1	< 0.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	105323	10.4%
i	97278	9.6%
n	91635	9.0%
a	83097	8.2%
t	79748	7.8%
o	74501	7.3%
s	64065	6.3%
r	62783	6.2%
c	48829	4.8%
l	41038	4.0%
Other values (34)	267656	26.3%

Common

Value	Count	Frequency (%)
	125213	> 99.9%
½	2	< 0.1%
²	1	< 0.1%
⁵	1	< 0.1%

Cyrillic

Value	Count	Frequency (%)
с	1	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1140893	> 99.9%
None	276	< 0.1%
Alphabetic PF	1	< 0.1%
Cyrillic	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	125213	11.0%
e	105323	9.2%
i	97278	8.5%
n	91635	8.0%
a	83097	7.3%
t	79748	7.0%
o	74501	6.5%
s	64065	5.6%
r	62783	5.5%
c	48829	4.3%
Other values (17)	308421	27.0%

None

Value	Count	Frequency (%)
ü	206	74.6%
ä	24	8.7%
ö	8	2.9%
é	7	2.5%
ß	6	2.2%
ó	4	1.4%
æ	3	1.1%
ç	2	0.7%
ł	2	0.7%
ã	2	0.7%
Other values (10)	12	4.3%

Alphabetic PF

Value	Count	Frequency (%)
ﬁ	1	100.0%

Cyrillic

Value	Count	Frequency (%)
с	1	100.0%

processed_description
Text

Distinct	25520
Distinct (%)	82.8%
Missing	0
Missing (%)	0.0%
Memory size	1.5 MiB

Length

Max length	328
Median length	288
Mean length	170.08122
Min length	3

Characters and Unicode

Total characters	5243264
Distinct characters	76
Distinct categories	5 ?
Distinct scripts	3 ?
Distinct blocks	4 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	24800 ?
Unique (%)	80.4%

Sample

1st row	go rapidly become preferred language building web services plenty tutorials available teach go syntax developers experience programming languages tutorials enough teach go idioms developers end recreating patterns make sense go context practical
2nd row	tidying messy software means breaking code make readable using guard clauses helping functions make understandable practical guide author kent beck creator extreme programming pioneer software patterns suggests might apply tidyings code
3rd row	recent series breakthroughs deep learning boosted entire field machine learning even programmers know close nothing technology use simple efficient tools implement programs capable learning data bestselling book uses concrete examples minimal theory production ready
4th row	data center many challenges system design today difficult issues need figured scalability consistency reliability efficiency maintainability addition overwhelming variety tools including relational databases nosql datastores stream batch processors message brokers
5th row	prepare microsoft exam ms help demonstrate real world mastery skills knowledge required deploy manage microsoft perform microsoft tenant level implementation administration cloud hybrid

Value	Count	Frequency (%)
book	13426	2.1%
conference	6090	0.9%
held	5634	0.9%
international	5477	0.8%
papers	5104	0.8%
proceedings	5007	0.8%
constitutes	4834	0.7%
data	4648	0.7%
th	4504	0.7%
nan	4232	0.6%
Other values (29465)	593601	91.0%

Most occurring characters

Value	Count	Frequency (%)
	621729	11.9%
e	592243	11.3%
i	374708	7.1%
n	356834	6.8%
s	344880	6.6%
t	342768	6.5%
a	330071	6.3%
o	316673	6.0%
r	314096	6.0%
c	226340	4.3%
Other values (66)	1422922	27.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	4621517	88.1%
Space Separator	621729	11.9%
Connector Punctuation	7	< 0.1%
Other Number	7	< 0.1%
Uppercase Letter	4	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	592243	12.8%
i	374708	8.1%
n	356834	7.7%
s	344880	7.5%
t	342768	7.4%
a	330071	7.1%
o	316673	6.9%
r	314096	6.8%
c	226340	4.9%
l	214184	4.6%
Other values (60)	1208720	26.2%

Other Number

Value	Count	Frequency (%)
²	4	57.1%
½	2	28.6%
¾	1	14.3%

Space Separator

Value	Count	Frequency (%)
	621729	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	7	100.0%

Uppercase Letter

Value	Count	Frequency (%)
ℤ	4	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	4621513	88.1%
Common	621748	11.9%
Greek	3	< 0.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	592243	12.8%
i	374708	8.1%
n	356834	7.7%
s	344880	7.5%
t	342768	7.4%
a	330071	7.1%
o	316673	6.9%
r	314096	6.8%
c	226340	4.9%
l	214184	4.6%
Other values (56)	1208716	26.2%

Common

Value	Count	Frequency (%)
	621729	> 99.9%
_	7	< 0.1%
²	4	< 0.1%
ℤ	4	< 0.1%
½	2	< 0.1%
µ	1	< 0.1%
¾	1	< 0.1%

Greek

Value	Count	Frequency (%)
α	1	33.3%
λ	1	33.3%
ω	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	5241346	> 99.9%
None	1897	< 0.1%
Alphabetic PF	17	< 0.1%
Letterlike Symbols	4	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	621729	11.9%
e	592243	11.3%
i	374708	7.1%
n	356834	6.8%
s	344880	6.6%
t	342768	6.5%
a	330071	6.3%
o	316673	6.0%
r	314096	6.0%
c	226340	4.3%
Other values (18)	1421004	27.1%

None

Value	Count	Frequency (%)
ü	743	39.2%
ä	497	26.2%
ö	231	12.2%
ß	81	4.3%
é	57	3.0%
â	40	2.1%
á	39	2.1%
ó	39	2.1%
ã	30	1.6%
í	18	0.9%
Other values (34)	122	6.4%

Alphabetic PF

Value	Count	Frequency (%)
ﬁ	10	58.8%
ﬀ	6	35.3%
ﬂ	1	5.9%

Letterlike Symbols

Value	Count	Frequency (%)
ℤ	4	100.0%

price_range
Categorical

HIGH CORRELATION IMBALANCE

Distinct	6
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	1.3 MiB

0-100	23575
101-500	7206
501-1000	42
1001-1500	2
1501-2000	2

Length

Max length	9
Median length	5
Mean length	5.472233
Min length	5

Characters and Unicode

Total characters	168698
Distinct characters	5
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	0-100
2nd row	0-100
3rd row	0-100
4th row	0-100
5th row	0-100

Common Values

Value	Count	Frequency (%)
0-100	23575	76.5%
101-500	7206	23.4%
501-1000	42	0.1%
1001-1500	2	< 0.1%
1501-2000	2	< 0.1%
2001-2500	1	< 0.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
0-100	23575	76.5%
101-500	7206	23.4%
501-1000	42	0.1%
1001-1500	2	< 0.1%
1501-2000	2	< 0.1%
2001-2500	1	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
0	92531	54.9%
1	38082	22.6%
-	30828	18.3%
5	7253	4.3%
2	4	< 0.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	137870	81.7%
Dash Punctuation	30828	18.3%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	92531	67.1%
1	38082	27.6%
5	7253	5.3%
2	4	< 0.1%

Dash Punctuation

Value	Count	Frequency (%)
-	30828	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	168698	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	92531	54.9%
1	38082	22.6%
-	30828	18.3%
5	7253	4.3%
2	4	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	168698	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	92531	54.9%
1	38082	22.6%
-	30828	18.3%
5	7253	4.3%
2	4	< 0.1%

month_of_sale
Real number (ℝ)

Distinct	12
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	6.9017452

Minimum	1
Maximum	12
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.5 MiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	4
median	7
Q3	10
95-th percentile	12
Maximum	12
Range	11
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	3.4385711
Coefficient of variation (CV)	0.49821762
Kurtosis	-1.1936179
Mean	6.9017452
Median Absolute Deviation (MAD)	3
Skewness	-0.14277669
Sum	212767
Variance	11.823771
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=12)

Value	Count	Frequency (%)
11	3122	10.1%
12	2998	9.7%
9	2847	9.2%
10	2797	9.1%
6	2618	8.5%
8	2604	8.4%
7	2544	8.3%
3	2538	8.2%
4	2319	7.5%
5	2308	7.5%
Other values (2)	4133	13.4%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	2188	7.1%
2	1945	6.3%
3	2538	8.2%
4	2319	7.5%
5	2308	7.5%
6	2618	8.5%
7	2544	8.3%
8	2604	8.4%
9	2847	9.2%
10	2797	9.1%

Value	Count	Frequency (%)
12	2998	9.7%
11	3122	10.1%
10	2797	9.1%
9	2847	9.2%
8	2604	8.4%
7	2544	8.3%
6	2618	8.5%
5	2308	7.5%
4	2319	7.5%
3	2538	8.2%

date_of_sale
Real number (ℝ)

Distinct	31
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	16.477845

Minimum	1
Maximum	31
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.5 MiB

Quantile statistics

Minimum	1
5-th percentile	2
Q1	8
median	17
Q3	25
95-th percentile	30
Maximum	31
Range	30
Interquartile range (IQR)	17

Descriptive statistics

Standard deviation	9.1850705
Coefficient of variation (CV)	0.55741941
Kurtosis	-1.2400813
Mean	16.477845
Median Absolute Deviation (MAD)	8
Skewness	-0.071273009
Sum	507979
Variance	84.365519
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=31)

Value	Count	Frequency (%)
1	1328	4.3%
30	1300	4.2%
28	1262	4.1%
29	1156	3.7%
19	1098	3.6%
31	1098	3.6%
25	1053	3.4%
6	1043	3.4%
27	1035	3.4%
15	1021	3.3%
Other values (21)	19434	63.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	1328	4.3%
2	836	2.7%
3	951	3.1%
4	812	2.6%
5	865	2.8%
6	1043	3.4%
7	931	3.0%
8	985	3.2%
9	960	3.1%
10	937	3.0%

Value	Count	Frequency (%)
31	1098	3.6%
30	1300	4.2%
29	1156	3.7%
28	1262	4.1%
27	1035	3.4%
26	1010	3.3%
25	1053	3.4%
24	1006	3.3%
23	926	3.0%
22	1015	3.3%

year_of_sale
Real number (ℝ)

HIGH CORRELATION

Distinct	42
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2015.3269

Minimum	1753
Maximum	2025
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.5 MiB

Quantile statistics

Minimum	1753
5-th percentile	2005
Q1	2012
median	2016
Q3	2020
95-th percentile	2023
Maximum	2025
Range	272
Interquartile range (IQR)	8

Descriptive statistics

Standard deviation	7.0266161
Coefficient of variation (CV)	0.0034865887
Kurtosis	502.80242
Mean	2015.3269
Median Absolute Deviation (MAD)	4
Skewness	-13.936303
Sum	62128499
Variance	49.373333
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=42)

Value	Count	Frequency (%)
2022	2354	7.6%
2018	2323	7.5%
2017	2234	7.2%
2016	2090	6.8%
2020	2068	6.7%
2021	2058	6.7%
2019	2047	6.6%
2013	1983	6.4%
2015	1914	6.2%
2014	1804	5.9%
Other values (32)	9953	32.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1753	8	< 0.1%
1980	1	< 0.1%
1986	4	< 0.1%
1987	1	< 0.1%
1988	5	< 0.1%
1989	10	< 0.1%
1990	12	< 0.1%
1991	15	< 0.1%
1992	11	< 0.1%
1993	20	0.1%

Value	Count	Frequency (%)
2025	21	0.1%
2024	316	1.0%
2023	1488	4.8%
2022	2354	7.6%
2021	2058	6.7%
2020	2068	6.7%
2019	2047	6.6%
2018	2323	7.5%
2017	2234	7.2%
2016	2090	6.8%

updated_price
Real number (ℝ)

HIGH CORRELATION

Distinct	1274
Distinct (%)	4.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	87.497879

Minimum	0
Maximum	2353.37
Zeros	108
Zeros (%)	0.4%
Negative	0
Negative (%)	0.0%
Memory size	1.5 MiB

Quantile statistics

Minimum	0
5-th percentile	26.94
Q1	49.99
median	72.64
Q3	94.44
95-th percentile	217.95
Maximum	2353.37
Range	2353.37
Interquartile range (IQR)	44.45

Descriptive statistics

Standard deviation	69.133299
Coefficient of variation (CV)	0.790114
Kurtosis	75.308757
Mean	87.497879
Median Absolute Deviation (MAD)	22.65
Skewness	4.7905115
Sum	2697384.6
Variance	4779.413
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
72.64	4441	14.4%
130.77	1796	5.8%
49.99	895	2.9%
203.42	716	2.3%
55.99	671	2.2%
44.99	669	2.2%
83.3	502	1.6%
63.82	467	1.5%
77.39	440	1.4%
76.5	419	1.4%
Other values (1264)	19812	64.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	108	0.4%
0.99	1	< 0.1%
1.33	6	< 0.1%
1.99	3	< 0.1%
2.68	8	< 0.1%
2.99	5	< 0.1%
3.99	9	< 0.1%
4.03	7	< 0.1%
4.23	3	< 0.1%
4.74	1	< 0.1%

Value	Count	Frequency (%)
2353.37	1	< 0.1%
1878.93	1	< 0.1%
1615.37	1	< 0.1%
1360.31	1	< 0.1%
1055.97	1	< 0.1%
935.21	1	< 0.1%
892.7	1	< 0.1%
850.2	1	< 0.1%
783.23	1	< 0.1%
781.2	1	< 0.1%

edition
Categorical

HIGH CORRELATION IMBALANCE MISSING

Distinct	21
Distinct (%)	0.7%
Missing	27974
Missing (%)	90.7%
Memory size	1.5 MiB

2	1653
3	540
4	260
5	160
6	90
Other values (16)	151

Length

Max length	2
Median length	1
Mean length	1.0157673
Min length	1

Characters and Unicode

Total characters	2899
Distinct characters	10
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	6 ?
Unique (%)	0.2%

Sample

1st row	2
2nd row	3
3rd row	8
4th row	5
5th row	2

Common Values

Value	Count	Frequency (%)
2	1653	5.4%
3	540	1.8%
4	260	0.8%
5	160	0.5%
6	90	0.3%
7	54	0.2%
8	32	0.1%
9	20	0.1%
10	14	< 0.1%
11	7	< 0.1%
Other values (11)	24	0.1%
(Missing)	27974	90.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2	1653	57.9%
3	540	18.9%
4	260	9.1%
5	160	5.6%
6	90	3.2%
7	54	1.9%
8	32	1.1%
9	20	0.7%
10	14	0.5%
12	7	0.2%
Other values (11)	24	0.8%

Most occurring characters

Value	Count	Frequency (%)
2	1663	57.4%
3	543	18.7%
4	265	9.1%
5	163	5.6%
6	91	3.1%
7	55	1.9%
1	50	1.7%
8	33	1.1%
9	22	0.8%
0	14	0.5%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2899	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
2	1663	57.4%
3	543	18.7%
4	265	9.1%
5	163	5.6%
6	91	3.1%
7	55	1.9%
1	50	1.7%
8	33	1.1%
9	22	0.8%
0	14	0.5%

Most occurring scripts

Value	Count	Frequency (%)
Common	2899	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
2	1663	57.4%
3	543	18.7%
4	265	9.1%
5	163	5.6%
6	91	3.1%
7	55	1.9%
1	50	1.7%
8	33	1.1%
9	22	0.8%
0	14	0.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2899	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
2	1663	57.4%
3	543	18.7%
4	265	9.1%
5	163	5.6%
6	91	3.1%
7	55	1.9%
1	50	1.7%
8	33	1.1%
9	22	0.8%
0	14	0.5%

affordability
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	1.5 MiB

Average price	18608
Low price	11317
Over priced	903

Length

Max length	13
Median length	13
Mean length	11.473012
Min length	9

Characters and Unicode

Total characters	353690
Distinct characters	15
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Average price
2nd row	Low price
3rd row	Average price
4th row	Average price
5th row	Low price

Common Values

Value	Count	Frequency (%)
Average price	18608	60.4%
Low price	11317	36.7%
Over priced	903	2.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
price	29925	48.5%
average	18608	30.2%
low	11317	18.4%
over	903	1.5%
priced	903	1.5%

Most occurring characters

Value	Count	Frequency (%)
e	68947	19.5%
r	50339	14.2%
	30828	8.7%
p	30828	8.7%
i	30828	8.7%
c	30828	8.7%
v	19511	5.5%
A	18608	5.3%
a	18608	5.3%
g	18608	5.3%
Other values (5)	35757	10.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	292034	82.6%
Space Separator	30828	8.7%
Uppercase Letter	30828	8.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	68947	23.6%
r	50339	17.2%
p	30828	10.6%
i	30828	10.6%
c	30828	10.6%
v	19511	6.7%
a	18608	6.4%
g	18608	6.4%
o	11317	3.9%
w	11317	3.9%

Uppercase Letter

Value	Count	Frequency (%)
A	18608	60.4%
L	11317	36.7%
O	903	2.9%

Space Separator

Value	Count	Frequency (%)
	30828	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	322862	91.3%
Common	30828	8.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	68947	21.4%
r	50339	15.6%
p	30828	9.5%
i	30828	9.5%
c	30828	9.5%
v	19511	6.0%
A	18608	5.8%
a	18608	5.8%
g	18608	5.8%
L	11317	3.5%
Other values (4)	24440	7.6%

Common

Value	Count	Frequency (%)
	30828	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	353690	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	68947	19.5%
r	50339	14.2%
	30828	8.7%
p	30828	8.7%
i	30828	8.7%
c	30828	8.7%
v	19511	5.5%
A	18608	5.3%
a	18608	5.3%
g	18608	5.3%
Other values (5)	35757	10.1%

Length_of_description
Real number (ℝ)

MISSING

Distinct	300
Distinct (%)	1.1%
Missing	4232
Missing (%)	13.7%
Infinite	0
Infinite (%)	0.0%
Mean	266.82223

Minimum	6
Maximum	350
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.5 MiB

Quantile statistics

Minimum	6
5-th percentile	203
Q1	244
median	248
Q3	335
95-th percentile	349
Maximum	350
Range	344
Interquartile range (IQR)	91

Descriptive statistics

Standard deviation	51.873812
Coefficient of variation (CV)	0.19441338
Kurtosis	1.0078702
Mean	266.82223
Median Absolute Deviation (MAD)	4
Skewness	0.079891517
Sum	7096404
Variance	2690.8924
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
250	2355	7.6%
249	2307	7.5%
248	2213	7.2%
247	2016	6.5%
246	1765	5.7%
245	1384	4.5%
350	1265	4.1%
244	1204	3.9%
349	997	3.2%
243	962	3.1%
Other values (290)	10128	32.9%
(Missing)	4232	13.7%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
6	1	< 0.1%
7	1	< 0.1%
9	4	< 0.1%
13	26	0.1%
14	1	< 0.1%
15	2	< 0.1%
17	1	< 0.1%
22	1	< 0.1%
23	1	< 0.1%
27	1	< 0.1%

Value	Count	Frequency (%)
350	1265	4.1%
349	997	3.2%
348	885	2.9%
347	748	2.4%
346	646	2.1%
345	507	1.6%
344	434	1.4%
343	358	1.2%
342	260	0.8%
341	194	0.6%

Auto

Heatmap
Table

	id	price	pub_year	num_of_author	month_of_sale	date_of_sale	year_of_sale	updated_price	Length_of_description	edition_num	availabe	price_range	edition	affordability
id	1.000	0.171	0.948	0.134	-0.009	0.046	0.952	0.171	-0.081	0.122	0.074	0.053	0.122	0.070
price	0.171	1.000	0.170	0.327	0.017	-0.080	0.156	1.000	-0.108	0.000	0.075	0.877	0.000	0.620
pub_year	0.948	0.170	1.000	0.112	-0.057	0.043	0.991	0.170	-0.078	0.069	0.010	0.039	0.069	0.070
num_of_author	0.134	0.327	0.112	1.000	0.011	-0.039	0.106	0.327	-0.121	0.239	0.000	0.018	0.239	0.054
month_of_sale	-0.009	0.017	-0.057	0.011	1.000	-0.014	-0.059	0.017	0.010	0.021	0.022	0.007	0.021	0.049
date_of_sale	0.046	-0.080	0.043	-0.039	-0.014	1.000	0.040	-0.080	0.007	0.005	0.100	0.029	0.005	0.080
year_of_sale	0.952	0.156	0.991	0.106	-0.059	0.040	1.000	0.156	-0.072	0.000	0.000	0.004	0.000	0.000
updated_price	0.171	1.000	0.170	0.327	0.017	-0.080	0.156	1.000	-0.108	0.000	0.075	0.877	0.000	0.620
Length_of_description	-0.081	-0.108	-0.078	-0.121	0.010	0.007	-0.072	-0.108	1.000	0.000	0.036	0.074	0.000	0.166
edition_num	0.122	0.000	0.069	0.239	0.021	0.005	0.000	0.000	0.000	1.000	0.000	0.000	1.000	0.031
availabe	0.074	0.075	0.010	0.000	0.022	0.100	0.000	0.075	0.036	0.000	1.000	0.037	0.000	0.056
price_range	0.053	0.877	0.039	0.018	0.007	0.029	0.004	0.877	0.074	0.000	0.037	1.000	0.000	0.379
edition	0.122	0.000	0.069	0.239	0.021	0.005	0.000	0.000	0.000	1.000	0.000	0.000	1.000	0.031
affordability	0.070	0.620	0.070	0.054	0.049	0.080	0.000	0.620	0.166	0.031	0.056	0.379	0.031	1.000

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

First rows
Last rows

	Title	id	price	author	publisher	pub_year	edition_num	description	availabe	sale_date	short_pub	num_of_author	width	height	processed_title	processed_description	price_range	month_of_sale	date_of_sale	year_of_sale	updated_price	edition	affordability	Length_of_description
0	Learning Go	211190367	71.990000	Jon Bodner	O'Reilly Media	2024	(2nd ed.)	Go has rapidly become the preferred language for building web services. Plenty of tutorials are available to teach Go's syntax to developers with experience in other programming languages, but tutorials aren't enough. They don't teach Go's idioms, so developers end up recreating patterns that don't make sense in a Go context. This practical...	True	2024-01-10	Jan 2024	1	97	150	learning go	go rapidly become preferred language building web services plenty tutorials available teach go syntax developers experience programming languages tutorials enough teach go idioms developers end recreating patterns make sense go context practical	0-100	1	10	2024	71.99	2	Average price	345.0
1	Tidy First?	211127822	42.990000	Kent Beck	O'Reilly Media	2023	NaN	Tidying up messy software is a must. And that means breaking up the code to make it more readable, and using guard clauses and helping functions to make it understandable. In this practical guide, author Kent Beck, creator of Extreme Programming and pioneer of software patterns, suggests when and where you might apply tidyings in your code. ...	True	2023-10-17	Oct 2023	1	97	150	tidy first	tidying messy software means breaking code make readable using guard clauses helping functions make understandable practical guide author kent beck creator extreme programming pioneer software patterns suggests might apply tidyings code	0-100	10	17	2023	42.99	NaN	Low price	346.0
2	Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow	210681725	96.990000	Aurélien Géron	O'Reilly Media	2022	(3rd ed.)	Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready...	True	2022-10-04	Oct 2022	1	97	150	hands on machine learning with scikit learn keras and tensorflow	recent series breakthroughs deep learning boosted entire field machine learning even programmers know close nothing technology use simple efficient tools implement programs capable learning data bestselling book uses concrete examples minimal theory production ready	0-100	10	4	2022	96.99	3	Average price	346.0
3	Designing Data-Intensive Applications	95729334	67.990000	Martin Kleppmann	O'Reilly Media	2017	NaN	Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What...	True	2017-03-16	Mar 2017	1	97	150	designing data intensive applications	data center many challenges system design today difficult issues need figured scalability consistency reliability efficiency maintainability addition overwhelming variety tools including relational databases nosql datastores stream batch processors message brokers	0-100	3	16	2017	67.99	NaN	Average price	347.0
4	Exam Ref MS-102 Microsoft 365 Administrator	210964419	53.862731	Orin Thomas	Pearson Education	2023	NaN	Prepare for Microsoft Exam MS-102 and help demonstrate your real-world mastery of skills and knowledge required to deploy and manage Microsoft 365 and perform Microsoft 365 tenant-level implementation and administration of cloud and hybrid...	True	2023-10-18	Oct 2023	1	97	150	exam ref ms microsoft administrator	prepare microsoft exam ms help demonstrate real world mastery skills knowledge required deploy manage microsoft perform microsoft tenant level implementation administration cloud hybrid	0-100	10	18	2023	53.86	NaN	Low price	242.0
5	CompTIA Network+ Certification All-in-One Exam Guide, Eighth Edition (Exam N10-008)	210456114	80.814300	Mike Meyers	McGraw Hill LLC	2022	(8th ed.)	This up-to-date Mike Meyers exam guide delivers complete coverage of every topic on the N10-008 version of the CompTIA Network+ Certification exam Get complete coverage of all the CompTIA Network+ exam objectives inside this comprehensive resource. Created and edited by Mike Meyers, the leading expert on CompTIA certification and training, ...	True	2022-02-11	Feb 2022	2	97	150	comptia network certification all in one exam guide eighth edition exam	date mike meyers exam guide delivers complete coverage every topic version comptia network certification exam complete coverage comptia network exam objectives inside comprehensive resource created edited mike meyers leading expert comptia certification training	0-100	2	11	2022	80.81	8	Average price	347.0
6	Exam Ref MD-102 Microsoft Endpoint Administrator	210901632	53.862731	Andrew Warren	Pearson Education	2023	NaN	Prepare for Microsoft Exam MD-102—and help demonstrate your real-world mastery of the skills and knowledge required to deploy, manage, and protect modern endpoints at scale in Microsoft 365 environments. Designed for endpoint administrators, this...	True	2023-08-18	Aug 2023	2	97	150	exam ref md microsoft endpoint administrator	prepare microsoft exam md help demonstrate real world mastery skills knowledge required deploy manage protect modern endpoints scale microsoft environments designed endpoint administrators	0-100	8	18	2023	53.86	NaN	Low price	249.0
7	The Staff Engineer's Path	210670147	42.990000	Tanya Reilly	O'Reilly Media	2022	NaN	For years, companies have rewarded their most effective engineers with management positions. But treating management as the default path for an engineer with leadership ability doesn't serve the industry well--or the engineer. The staff engineer's path allows engineers to contribute at a high level as role models, driving big projects,...	True	2022-09-20	Sep 2022	1	97	150	the staff engineer path	years companies rewarded effective engineers management positions treating management default path engineer leadership ability serve industry engineer staff engineer path allows engineers contribute high level role models driving big projects	0-100	9	20	2022	42.99	NaN	Low price	340.0
8	Learning Web Design	96260103	67.990000	Jennifer Robbins	O'Reilly Media	2018	(5th ed.)	Do you want to build web pages but have no prior experience? This friendly guide is the perfect place to start. You’ll begin at square one, learning how the web and web pages work, and then steadily build from there. By the end of the book, you’ll have the skills to create a simple site with multicolumn pages that adapt for mobile devices. Each...	True	2018-06-05	May 2018	1	97	150	learning web design	want build web pages prior experience friendly guide perfect place start begin square learning web web pages work steadily build end book skills create simple site multicolumn pages adapt mobile devices	0-100	6	5	2018	67.99	5	Average price	350.0
9	Deciphering Data Architectures	211219932	84.990000	James Serra	O'Reilly Media	2024	NaN	Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion. This practical book provides a guided tour of each architecture to help data professionals understand its pros and...	True	2024-02-06	Feb 2024	1	97	150	deciphering data architectures	data fabric data lakehouse data mesh recently appeared viable alternatives modern data warehouse new architectures solid benefits surrounded lot hyperbole confusion practical book provides guided tour architecture help data professionals understand pros	0-100	2	6	2024	84.99	NaN	Average price	346.0

	Title	id	price	author	publisher	pub_year	edition_num	description	availabe	sale_date	short_pub	num_of_author	width	height	processed_title	processed_description	price_range	month_of_sale	date_of_sale	year_of_sale	updated_price	edition	affordability	Length_of_description
39972	Conceptual Structures: Common Semantics for Sharing Knowledge	2474901	72.641169	Frithjof Dau	Springer Berlin Heidelberg	2005	NaN	NaN	True	2005-07-11	Jul 2005	3	97	150	conceptual structures common semantics for sharing knowledge	nan	0-100	7	11	2005	72.64	NaN	Average price	NaN
39973	Learning Theory	2474907	130.765729	Peter Auer	Springer Berlin Heidelberg	2005	NaN	This volume contains papers presented at the Eighteenth Annual Conference on Learning Theory (previously known as the Conference on Computational Learning Theory) held in Bertinoro, Italy from June 27 to 30, 2005. The technical program contained...	True	2005-06-28	Jun 2005	2	97	150	learning theory	volume contains papers presented eighteenth annual conference learning theory previously known conference computational learning theory held bertinoro italy june technical program contained	101-500	6	28	2005	130.77	NaN	Average price	248.0
39974	Advances in Knowledge Discovery and Data Mining	2474931	130.765729	Tu Bao Ho	Springer Berlin Heidelberg	2005	NaN	The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the area of data mining and knowledge discovery. It provides an international forum for researchers and industry practitioners to...	True	2005-05-13	May 2005	3	97	150	advances in knowledge discovery and data mining	pacific asia conference knowledge discovery data mining pakdd leading international conference area data mining knowledge discovery provides international forum researchers industry practitioners	101-500	5	13	2005	130.77	NaN	Average price	248.0
39975	Charting the Topic Maps Research and Applications Landscape	2476374	72.641169	Lutz Maicher	Springer Berlin Heidelberg	2006	NaN	This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Topic Map Research and Applications, held in October 2005. The 17 revised full papers and five revised short papers presented together with one...	True	2006-02-15	Feb 2006	2	97	150	charting the topic maps research and applications landscape	book constitutes thoroughly refereed post proceedings first international workshop topic map research applications held october revised full papers five revised short papers presented together	0-100	2	15	2006	72.64	NaN	Average price	248.0
39976	Data Mining for Biomedical Applications	2476412	72.641169	Jinyan Li	Springer Berlin Heidelberg	2006	NaN	This book constitutes the refereed proceedings of the International Workshop on Data Mining for Biomedical Applications, BioDM 2006, held in Singapore in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining...	True	2006-02-28	Feb 2006	3	97	150	data mining for biomedical applications	book constitutes refereed proceedings international workshop data mining biomedical applications biodm held singapore conjunction th pacific asia conference knowledge discovery data mining	0-100	2	28	2006	72.64	NaN	Average price	245.0
39977	Finite-State Methods and Natural Language Processing	2476527	72.641169	Anssi Yli-Jyrä	Springer Berlin Heidelberg	2006	NaN	This book constitutes the thoroughly refereed post-proceedings of the 5th International Workshop on Finite-State Methods in Natural Language Processing, FSMNLP 2005, held in Helsinki, Finland, September 2005. The book presents 24 revised full...	True	2006-12-12	Dec 2006	3	97	150	finite state methods and natural language processing	book constitutes thoroughly refereed post proceedings th international workshop finite state methods natural language processing fsmnlp held helsinki finland september book presents revised full	0-100	12	12	2006	72.64	NaN	Average price	245.0
39978	Argumentation in Multi-Agent Systems	2476617	72.641169	Simon D. Parsons	Springer Berlin Heidelberg	2006	NaN	NaN	True	2006-07-25	Jul 2006	4	97	150	argumentation in multi agent systems	nan	0-100	7	25	2006	72.64	NaN	Average price	NaN
39979	PRICAI 2006: Trends in Artificial Intelligence	2476718	232.483709	Quiang Yang	Springer Berlin Heidelberg	2008	NaN	This book constitutes the refereed proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2006, held in Guilin, China in August 2006. The book presents 81 revised full papers and 87 revised short papers...	True	2008-02-20	Feb 2008	2	97	150	pricai trends in artificial intelligence	book constitutes refereed proceedings th pacific rim international conference artificial intelligence pricai held guilin china august book presents revised full papers revised short papers	101-500	2	20	2008	232.48	NaN	Average price	245.0
39988	Knowledge Science, Engineering and Management	2476736	130.765729	Jérôme Lang	Springer Berlin Heidelberg	2006	NaN	Here are the refereed proceedings of the First International Conference on Knowledge Science, Engineering and Management, KSEM 2006, held in Guilin, China in August 2006 in conjunction with PRICAI 2006. The book presents 51 revised full papers and...	True	2006-07-25	Jul 2006	3	97	150	knowledge science engineering and management	refereed proceedings first international conference knowledge science engineering management ksem held guilin china august conjunction pricai book presents revised full papers	101-500	7	25	2006	130.77	NaN	Average price	250.0
39989	Artificial Neural Networks in Pattern Recognition	2476777	72.641169	Friedhelm Schwenker	Springer Berlin Heidelberg	2006	NaN	This book constitutes the refereed proceedings of the Second IAPR Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2006, held in Ulm, Germany in August/September 2006. The 26 revised papers presented were carefully reviewed and...	True	2006-08-29	Aug 2006	2	97	150	artificial neural networks in pattern recognition	book constitutes refereed proceedings second iapr workshop artificial neural networks pattern recognition annpr held ulm germany august september revised papers presented carefully reviewed	0-100	8	29	2006	72.64	NaN	Average price	250.0

Overview

Variables

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Other Punctuation

Decimal Number

Math Symbol

Open Punctuation

Dash Punctuation

Other Symbol

Other Number

Space Separator

Close Punctuation

Final Punctuation

Initial Punctuation

Modifier Symbol

Format

Currency Symbol

Most occurring scripts

Most frequent character per script

Latin

Common

Cyrillic

Most occurring blocks

Most frequent character per block

ASCII

Punctuation

None

Letterlike Symbols

Math Operators

Currency Symbols

Cyrillic

Alphabetic PF

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Decimal Number

Other Punctuation

Math Symbol

Final Punctuation

Format

Space Separator

Dash Punctuation

Close Punctuation

Open Punctuation

Other Symbol

Nonspacing Mark

Currency Symbol

Initial Punctuation

Other Number

Modifier Symbol

Most occurring scripts

Most frequent character per script

Latin

Common

Inherited

Most occurring blocks

Most frequent character per block

ASCII

None

Punctuation

Math Operators

Diacriticals

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Other Punctuation

Space Separator

Dash Punctuation

Math Symbol

Open Punctuation

Close Punctuation

Most occurring scripts